Michele Lanzetta

Scaling Hard Vertical Surfaces with Compliant Microspine Arrays

A new approach for climbing hard vertical surfaces has been developed that allows a robot to scale concrete, stucco, brick and masonry walls without using suction or adhesives.The approach is inspired by the mechanisms observed in some climbing insects and spiders and involves arrays of microspines that catch on surface asperities. The arrays are located on the toes of the robot and consist of a tuned, multi-link compliant suspension. The fundamental issues of spine allometric scaling versus surface roughness are discussed and the interaction between spines and surfaces is modeled. The toe suspension properties needed to maximize the probability that each spine will find a useable surface irregularity and to distribute climbing loads among many spines are detailed. The principles are demonstrated with a new climbing robot, SpinybotII, that can scale a wide range of flat exterior walls, carry a payload equal to its own weight, and cling without consuming power. The paper also reports how toe parameters scale with robot mass and how spines have also been used successfully on the larger RiSE robot.

A new flexible high-resolution vision sensor for tool condition monitoring

Abstract From a critical review of defect morphology and image analysis techniques from the literature it seems that a method to recognise any kind of defect and the algorithms to measure all wear types are not available. This article is divided into two main parts: (i) a possible exhaustive classification of defects in cutting inserts and (ii) the design of an automated sensor to recognise defects and to measure wear. The morphology characterisation has led to the definition of a limited number of classes and recognition criteria that occur for different types of cutting materials and working conditions for milling and turning operations. They represent the main requirements of recognition and measurement algorithms. The global logic flow for decision making is also provided. The sensor configuration is outlined with the necessary views and lighting devices. The identification of the worn out areas is performed by software segmentation to detect the texture differences between damaged and undamaged zones and has been tested on different types of carbide inserts. A resolution enhancement method is also proposed.

Improved surface finish in 3D printing using bimodal powder distribution

The use of bimodal powders has been shown to offer the possibility of dramatically improved surface finish in 3D printing. This work focused on individual lines, the primitive building block of 3D printed parts. It was observed that the fine component of bimodal ceramic powders, while uniformly distributed in the original powderbed, was preferentially found at the surface of the printed line, while the interior of the line was denuded of fines. Microscopic examination and approximate quantitative analysis supports the assertion that essentially all the fines have moved to the surface of the line. The mechanism for this rearrangement is not known, but is speculated to be related to the relative difficulty of wetting fine powders. The parameter space in which this phenomenon can be observed was examined in a preliminary manner.

Heuristics for scheduling a two-stage hybrid flow shop with parallel batching machines: application at a hospital sterilisation plant

The model of a two-stage hybrid (or flexible) flow shop, with sequence-independent uniform setup times, parallel batching machines and parallel batches has been analysed with the purpose of reducing the number of tardy jobs and the makespan in a sterilisation plant. Jobs are processed in parallel batches by multiple identical parallel machines. Manual operations preceding each of the two stages have been dealt with as machine setup with standardised times and are sequence-independent. A mixed-integer model is proposed. Two heuristics have been tested on real benchmark data from an existing sterilisation plant: constrained size of parallel batches and fixed time slots. Computation experiments performed on combinations of machines and operator numbers suggest balancing the two stages by assigning operators proportionally to the setup time requirements.

Scheduling flow lines with buffers by ant colony digraph

This work starts from modeling the scheduling of n jobs on m machines/stages as flowshop with buffers in manufacturing. A mixed-integer linear programing model is presented, showing that buffers of size n-2 allow permuting sequences of jobs between stages. This model is addressed in the literature as non-permutation flowshop scheduling (NPFS) and is described in this article by a disjunctive graph (digraph) with the purpose of designing specialized heuristic and metaheuristics algorithms for the NPFS problem. Ant colony optimization (ACO) with the biologically inspired mechanisms of learned desirability and pheromone rule is shown to produce natively eligible schedules, as opposed to most metaheuristics approaches, which improve permutation solutions found by other heuristics. The proposed ACO has been critically compared and assessed by computation experiments over existing native approaches. Most makespan upper bounds of the established benchmark problems from Taillard (1993) and Demirkol, Mehta, and Uzsoy (1998) with up to 500 jobs on 20 machines have been improved by the proposed ACO.

Native metaheuristics for non-permutation flowshop scheduling

The most general flowshop scheduling problem is also addressed in the literature as non-permutation flowshop (NPFS). Current processors are able to cope with the

Optimum Dataset Size and Search Space for Minimum Zone Roundness Evaluation by Genetic Algorithm

(n!)^{m}

Computer-Aided Visual Inspection in Assembly

combinatorial complexity of NPFS scheduling by metaheuristics. After briefly discussing the requirements for a manufacturing layout to be designed and modeled as non-permutation flowshop, a disjunctive graph (digraph) approach is used to build native solutions. The implementation of an Ant Colony Optimization (ACO) algorithm has been described in detail; it has been shown how the biologically inspired mechanisms produce eligible schedules, as opposed to most metaheuristics approaches, which improve permutation solutions. ACO algorithms are an example of native non-permutation (NNP) solutions of the flowshop scheduling problem, opening a new perspective on building purely native approaches. The proposed NNP-ACO has been assessed over existing native approaches improving most makespan upper bounds of the benchmark problems from Demirkol et al. (1998).